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GeoPlanning 
Journal of Geomatics and Planning                                                                                                                Vol. 10, No. 1, 2023     

 

Original Research 

An Application of Cellular Automata (CA) and 

Markov Chain (MC) Model in Urban Growth 

Prediction: A case of Surat City, Gujarat, India 

Kaushikkumar P. Sheladiya1*, Chetan R. Patel1 

1. Urban Planning Section, Department of Civil Engineering, S. V. National Institute of 

Technology,Surat,Gujarat,India 

DOI: 10.14710/geoplanning.10.1.23-36 

Abstract 

The main purpose of this study is to detect land use land cover change for 1990-2000, 2000-2010, and 2010-2020 using 

multispectral Landsat images as well as to simulate and predict urban growth of Surat city using Cellular Automata-based 

Markov Chain Model. Maximum likelihood supervise classification was used to generate LULC maps of the years 

1990,2000,2010, and 2020 and the overall accuracy of these maps were 90%, 95%, 91.25%, and 96.25%, respectively. Two 

transition rules were commuted to predict the LULC of 2010 and 2020. For validation of these LULC maps, the Area Under 

Characteristics curve was used, and these maps' accuracy was 95.30% and 86.90%. This validation predicted LULC maps 

for the years 2035 and 2050. Transition rules of 2010-2035 showed that there will be a probability that 36.33% of vegetation 

area and 40.27% of the vacant land area will be transited into built-up by the year 2035, and it will be 49.20 % of the total 

area. Also, 57.77% of the vegetation area and 60.24% of the built-up area will be transformed into urban areas by the year 

2050, almost 62.60 %. Analysis of LULC maps 2035 and 2050 exhibits that there will be abundant growth in all directions 

except the South Zone and Southwest Zone. Therefore, this study helps urban planners and decision-makers decide what 

to retain, where to plan for new development and type of development, what to connect, and what to protect in coming 

years. 

Copyright © 2023 GJGP-Undip 

This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license 

1. Introduction  

The current demographic transition from rural to urban areas is the most considerable shift of this century, 

bringing planning and development policy for micro and macro development (Chaudhuri & Clarke, 2019). This 

evaluation of the demographic transition process starts with the formation of towns and cities, and then it takes 

the size of metropolitan and urban agglomerations (Deep, 2014; Sahana et al., 2018). Urban sprawl has become 

a worldwide problem, especially for a developing nation. An indication of imbalance between urban spatial 

expansion and underlying population can characterize urban sprawl. Reasons behind the urban sprawl are high 

population growth, high accessibility to urban areas from suburban areas, and choice of people to live near peri-

urban areas. Urban sprawl usually covers vegetation land, resulting in biodiversity loss and the heat island effect. 

So, assessing the impact of different land use planning schemes and development policies is essential to optimize 

the loss of natural land (Gao et al., 2020; Jokar Arsanjani et al., 2013).  

Town planners generally used zoning to differentiate land use as a method of guiding and controlling the 

growth of urban areas. In the early development phase, this concept was applied in the planning of developed 

countries and is now in developing countries (He et al., 2018). Developing strategies for evaluating various urban 

development scenarios about potential implications for land use and the advancement of existing spatial plans 

and policies is vital for urban and regional planners (Al-Ahmadi et al., 2009). Stakeholders, such as those involved 

 
e-ISSN: 2355-6544 
 
Received: 06 February 2023; 
Accepted: 31 October 2023; 
Published: 31 October 2023. 
 
Keywords:  
Cellular Automata(CA), Markov 
Chain(MC), Urban Growth 
Modelling, Geographic Information 
System, Surat City 
 
*Corresponding author(s)  
email: chetanrpatel@rediffmail.com 
 
 
 

 

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mailto:chetanrpatel@rediffmail.com


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in research, modeling, forecasting, and policymaking related to planning for sustainable urban growth, are also 

concerned about the effects of piecemeal planning in large cities. However, the urbanization and urban 

development phase worldwide does not follow a uniform pattern. In developed countries, the concentration of 

population in medium-sized cities has risen dramatically. Most small and medium-sized cities in India are 

expected to be of regional significance by 2030. As a result, these cities will strengthen their socioeconomic 

conditions and infrastructure and serve the more extensive hinterland. 

Land use land cover change is a complex and dynamic system resulting from spatial interaction between 

different land uses over some time. This has become a key process in optimizing land use in a step towards the 

development of sustainable smart cities (Deep, 2014; Shu et al., 2020). The urban system requires integrated 

tools that help guide and forecast urban growth because of its complex and dynamic nature. This time series 

dynamic process has complex interactions between land use, transportation, population, economy, river, 

topography, growth policies, culture, and politics. Planners have tried to project and guide urban growth using 

several simulation models (Gharaibeh et al., 2020; Mustafa et al., 2017; Thapa & Murayama, 2020; Tripathy & 

Kumar, 2019). 

In urban planning, many researchers and decision-makers have applied different methods and models to 

simulate and predict urban sprawl. Remote sensing (RS) and Geographic Information systems (GIS) are some of 

the most used to detect spatial and temporal changes (Deep, 2014; Gharaibeh et al., 2020; Sahana et al., 2018; 

Tripathy & Kumar, 2019). It can be analyzed by modeling urban growth using different models like cellular 

automata (CA) (Mustafa et al. 2017; Xu & Gao, 2019), logistics regression (Okafor et al., 2020), Markov chain 

technique (Lu et al., 2018; Mosammam et al., 2017), SLEUTH (slope, land use, exclusion, urban extent, 

transportation, and hill shade), Fuzzy, genetic algorithm (Li et al., 2008), AHP (analytical hierarchy process) 

(Aburas et al., 2016), ANN (arithmetic neural network) (Gharaibeh et al., 2020), weights of evidence, conversion 

of land use and its effects (CLUE), entropy optimization (Gao et al., 2020) and land use transformation model to 

predict urban growth.  

Out of all models, the CA model is used widely because of its flexibility, ability to integrate the spatial and 

temporal dimensions of the process, and to model complex dynamic systems (Aburas et al., 2016). CA model is a 

discrete, repetitive, and dynamic system in which the state of each cell depends on previous conditions as well as 

the condition of the neighborhood (Lagarias, 2012). CA model can simulate and predict urban growth based on 

the assumption that past urban growth and local and regional interactions of different land uses. It is most 

suitable for the simulation of a spatial pattern. However, it does not help interpret urban growth because it 

cannot predict and simulate spatial changes (Hu & Lo, 2007).  

Markov Chain (MC) model can quantify temporal changes in land use classes from one stage to another 

using a transition probability matrix but not spatial changes. In contrast, the CA automata model can predict 

and simulate spatial changes. MC model doesn't consider the effect of surrounding cells but only considers the 

states of cells at a different two-time period while quantifying land-use changes (Okafor et al., 2020). The ability 

to articulate time shifts from one time to another makes MC the best tool to model land-use changes and thus 

provides a framework for forecasting future changes. To overcome the limitation of both the MC and CA model, 

it is better to integrate the CA and MC model to identify spatial-temporal changes and quantify those changes 

for simulation and projection of urban growth. The integrated CA – MC model has been the most common 

method of simulating transition in urban development and land use over the last 10 years, perhaps because it 

does not demand a considerable amount of data, and the model itself is user-friendly, even for users who are not 

specialists (Milad et al., 2016). 

Surat City has experienced unprecedented urban growth in the last three decades. Due to commercial and 

business activities, many workers migrated to Surat from surrounding states like Rajasthan, Uttar Pradesh, 

Madhya Pradesh, Maharashtra, and Bihar. It steered its natural resources like agricultural land and water bodies. 

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Therefore, this study was carried out to detect spatial changes in land use land cover from the year 1990 to 2020 

to analyze the urban growth direction of the city as well as to predict urban growth for the years 2035 and 2050. 

This paper comprises five sections. Section 2 discusses the functionality of the CA-based Markov Chain 

model, followed by section 3, which is about the study area profile and data used for the study. Section 4 comprises 

an analysis of maps and results of actual and predicted LULC maps. Section 5 concludes this study's applicability 

and future scope with advanced machine learning models.  

2. Data and Methods 

2.1. Study Area Profile 

Surat is well-known for its major diamond polishing industries and the textile hub of Gujarat state of 

India. The city is located between latitudes 21º03' and 21º19' North and longitudes 72º41' and 73º00' East, having 

an area of 326.53 sq.km, as shown in Figure 1. It is 13 m above the mean sea level. Surat City has a population 

of 4.46 million and population density was 13,680 persons per sq.km as per census 2011. The city is located in 

the southern part of Gujarat state in western India. It lies near the mouth of the Tapti River in the Gulf of 

Khambhat (Cambay). The city has grown on both sides of river Tapti. Surat city is considered one of the fastest-

growing cities in India. Surat city has a high migration rate from different parts of Gujarat and other states of 

India because of the diamond and textile industries. It is well connected by the Ahmedabad-Mumbai corridor. 

Surat has experienced rapid urbanization and expansion of urban boundaries due to its fast-growing population. 

Thus, future growth should be confined within the specified urban form delimited by urban growth boundaries 

to preserve agricultural areas and prevent urban sprawl. 

 

Figure 1. The Map of Surat City 

2.2. Methodology of Cellular Automata (CA) and Markov Chain (MC) Model  

Four satellite images were used to extract the land use maps for Surat City as part of the materials and 

methodology used in this study. The maximum likelihood classification technique, a supervised classification 

method, was used to classify images. Accurate polygons were chosen as training and study areas in order to 

classify the images. Four classes were used: built-up, agriculture, vacant land and water. Therefore, the 

resampling step was conducted after image classification. 

Consequently, the analysis, simulation and future land use change prediction were conducted in the 

IDRISI-Selva software environment. Specifically, the number of land use classes and their changes in periods 

(1990, 2000, 2010 and 2020) was calculated using cross-tabulation analysis. Afterward, the CA–Markov model 

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was applied to simulate and predict future land use changes in Surat city, as shown in the flowchart presented in 

Figure 2. 

2.2.1. Cellular Automata (CA) Model 

Generally, CA models aim to simulate the actual nature regulations. Land use change modeling using the 

CA technique is a preferred method because it gives explicit spatial modeling results based on a defined transition 

rule (Ward et al., 2000; White & Engelen, 2000). Moreover, CA automata model types are suitable to represent, 

analyze and forecast geographic processes due to the relationships among a raster grid (Mitsova et al., 2011). 

The CA automata are a practical tool in urban system simulations since population and land use change can be 

presented together. 

Furthermore, cells of the cellular lattice can be aggregated efficiently with economic and transportation 

data. Thus, the urban areas can be effectively simulated by using proper neighborhoods of cells on the cellular 

grid. Moreover, theories of the urbanization process can be examined based on used spatial models (Mitsova et 

al., 2011). The CA model has been used increasingly in land use change and urban expansion modeling (He et 

al., 2006). It is worth mentioning that the time and space in CA model are considered as discrete units, and the 

space is considered as a regular grid (lattice) in two dimensions. The main aspect of CA model is the local 

interactions which reflect the dynamicity of system evolution (Wang et al., 2012). CA models are able to simulate 

stochastic, nonlinear and spatial processes. Many studies have illustrated that CA models have the potential to 

model the complex spatiotemporal process of land use change, urban systems and its patterns in an 

understandable manner (Barredo & Demicheli, 2003; He et al., 2006; Wang et al., 2012; Xian & Crane, 2005). 

The significant components of CA models are as follows: (a) cells, (b) cell neighborhoods and (c) transition 

rules, i.e., the cell is the fundamental element of the automation system, i.e., the cell are organized in a lattice. 

The transition rule that defines the state of each cell for the coming time step depends on the current state of 

that cell and its surrounding neighborhood cells. After that, a land use change suitability map is required and 

the dynamics should be defined in the system. The primary expression of the CA model can be expressed as: 

𝑆(𝑡, 𝑡 + 1) = 𝑓(𝑆(𝑡), 𝑁)……………………. Eq. (1) 

where S is the states of discrete cellular, t is the time instant, t +1 is the coming future time instant, N is the 

cellular field and f is the transition rule of cellular states in local space. 

2.2.2. Markov Chain (MC) Model 

Markov chain model is based on the progression of the formation of Markov stochastic process systems 

for predicting one status being changed to another. The Markov chain model is commonly used to model and 

simulate changes, dimensions and trends of land use/cover (Sang et al., 2011; Weng, 2002). Markov chain model 

analyses and summarizes the change in land use by a number of probabilities transition areas from one status to 

a different status over a specified period. Additionally, the produced probabilities transition areas can be used to 

predict and discover the probable scenarios of future land use change and urban growth patterns. On the other 

hand, the Markov chain cannot model and simulate the changes in spatial distribution. Nevertheless, it is an 

effective and powerful model that can estimate and predict the quantity of land use change (Xin et al., 2012). The 

prediction of future land use changes can be calculated based on the conditional probability formula by using the 

following equation: 

𝑆(𝑡 + 1) = Pij x 𝑆(𝑡)……………..Eq. (2) 

Pij =  (

𝑃11 𝑃12 𝑃1𝑛

𝑃21 𝑃22 𝑃2𝑛

𝑃𝑛1 𝑃𝑛2 𝑃𝑛𝑛

)…………..Eq. (3) 

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And ( 0 ≤ Pij < 1 and ∑ 𝑃𝑖𝑗
𝑁
𝑗=1  = 1, (i, j = 1,2,……..n) 

where S (t) is the state of the system at time t, S (t +1) is the state of the system at time (t +1); Pij is the matrix 

of transition probability in a state. 

2.2.3. Validation of CA-MC Model by Receiver Operating Characteristic Curve (ROC) 

Relative Operating Characteristics is an excellent method to assess the validity of a model that predicts 

the location of the occurrence of a class by comparing a suitability image depicting the likelihood of that class 

occurring. This technique compares predicted results with actual results and plot percentages of true positives 

against the percentage of false positives at a predefned threshold value (Hu & Lo, 2007; Sarkar & Chouhan, 2020). 

The ROC calculates the area under the curve (AUC), which threshold value lies in between 0 to 1, where 1 

denotes a perfect match and 0 denote complete miss-match. By using this validation technique, LULC maps for 

year 2035 and 2050 were forecasted. 

2.3. Preparation of Landuse Maps and Land Use Land Cover Change (LULC) Analysis 

To perform Landuse land cover change analysis, Landsat 8 satellite images were downloaded from the 

portal of the United States Geological Survey (https://earthexplorer.usgs.gov/) of years 1990,2000,2010 and 

2020 at no cost as shown in Table 1. After doing spatial, radiometric, and spectral corrections of satellite images, 

they were applied for LULC analysis. To supervise image classification, an image classification tool was used to 

generate a training sample of each land cover, which includes built-up, agriculture, vacant land, and water bodies. 

Then, final land use land cover raster  maps were generated using the maximum likelihood classification tool for 

the years 1990,2000,2010, and 2020, as shown in Figure 3. For ground truth verification, twenty-five points 

were considered for each land cover using Google Earth Pro software to check the accuracy of classified LULC 

images. From that, the Confusion matrix was generated by cross-verifying twenty-five stratified random points 

of each land cover with ground truth points. Afterward, user accuracy, producer accuracy, and Kappa coefficient 

were calculated from the confusion matrix for LULC maps at 0.87, 0.93,0.88, and 0.95, respectively, for the years 

1990,2000,2010, and 2020. The overall accuracy of maps was 90%, 95%, 91.25%, and 96.25%, respectively, which 

indicates that these LULC maps can be used for further processing of land use assessment.  

 

Figure 2. Flow chart of Applied CA Markov Model 

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Table 1. Metadata of Satellite Images 

Year Dataset  Sensor Path/Row 
1990 Landsat 5  TM 

148/05 
2000 Landsat 5  TM 
2010 Landsat 5  TM 
2020 Landsat 8  OLI/TIRS 

 

Figure 3. Land Use Land Cover Maps for the years 1990, 2000, 2010 and 2020 

In 1990, 20.77 sq. km area was covered by built-up, 125.51 sq. km by vegetation, 155.62 sq. km by vacant 

land, and 17.23 sq. km by water bodies. In 2000, there was a gain in the built-up area by 140.25 % and it became 

49.9 sq. km, while a loss in vegetation area by 11.26% and vacant land area by 15.58%. The built-up area was 

further increased to 35.27 %, which was 67.5 sq. km, vegetation area decreased by 20.18 %, which became 88.9 

sq. km and vacant land area was increased by 8.99 % and it was 143.19 sq. km in the year 2010. A huge 

development took place in Surat city area from 2010 to 2020, including the outer ring road in the Surat Urban 

Development Authority area, the starting phase of Surat Metro and Diamond Burge in Khajod area, good public 

transport infrastructure in terms of Bus Rapid Transit System (BRTS) and Sitilink. These steps attracted many 

workers from the surrounding area for employment in the textile and diamond industries. Due to this, the built-

up area increased by 78.66 % and became 120.6 sq. km, vegetation area again decreased by 39.65%, and vacant 

land area decreased by 21.13 % in 2020. 

3. Result and Discussion 

3.1 Transition Rules 

The transition probability matrix records the number of pixels expected to change from each land cover 

type to each other over the specified number of time units. The transition areas matrix was calculated using the 

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Markov model of IDRISI Selva for the years 1990-2000, 2000-2010 and 2010-2020. There is a probability that 

3.29% of agriculture areas will be expected to change into built-up areas, 32.14% of areas into vacant land and 

4.33% of areas into water bodies, while 60.23% of areas expect to persist in the year 2000. In vacant land, 17.97% 

of the areas will be transformed to build up, 4.92% of areas into water, while 55.44% expect to remain as it is in 

the year 2000. Regarding water bodies, 13.15% will convert into vacant land in 2000, as shown in Table 2. 

Table 2. Transition Probability of Changing Land Use Land Cover Class from 1990-2000 

Land use/ Land cover Built-up Agriculture Vacant Land Water 
Built-up 99.20% 0.60% 0.63% 0.11% 

Agriculture 3.29% 60.23% 32.14% 4.33% 
Vacant Land 17.97% 21.67% 55.44% 4.92% 

Water 1.18% 10.63% 13.15% 75.04% 

There is a probability that 5.17% of agriculture areas will be expected to change into built-up areas, 45.04% 

of areas into vacant land and 1.59% areas into water bodies. In comparison, 48.20% of areas are expected to 

persist in 2010. In vacant land, 16.47% of areas will be transformed to build up, 1.82% of areas into the water, 

while 61.98% expect to remain as it is in 2010. Regarding water bodies, 27.03% will be converted into vacant 

land in 2010, as shown in Table 3. 

Table 3. Transition Probability of Changing land use land cover class from 2000-2010 

Land use/ Land cover Built-up Agriculture Vacant Land Water 
Built-up 97.58% 1.38% 1.03% 0.01% 

Agriculture 5.17% 48.20% 45.04% 1.59% 
Vacant Land 16.47% 19.73% 61.98% 1.82% 

Water 1.94% 12.90% 27.03% 58.13% 

There is a probability that 25.49% of agriculture areas will be expected to change into built-up areas, 

32.39% of areas into vacant land and 2.70% areas into water bodies. In comparison, 39.43% of areas are expected 

to persist in 2020. In vacant land, 29.37% of areas will be transformed to build-up, 3.05% of areas into water, 

while 49.93% expect to remain as it is in 2020. In the case of water bodies, 7.34% will convert into vacant land 

and 9.48% into agriculture in 2020, as shown in Table 4. 

Table 4. Transition Probability of Changing Land Use Land Cover Class from 2010-2020 

Land use/ Land cover Built-up Agriculture Vacant Land Water 
Built-up 97.03% 0.34% 2.53% 0.10% 

Agriculture 25.49% 39.43% 32.39% 2.70% 
Vacant Land 29.37% 17.64% 49.93% 3.05% 

Water 8.16% 9.48% 7.34% 75.02% 

Results should be clear and concise. The results should summarize (scientific) findings rather than provide 

data in great detail. Please highlight differences between your results or findings and the previous publications 

by other researchers. For tables, they are sequentially numbered with the table title and number above the table. 

Tables should be centered in the column and fit to the window. 

3.2 Prediction of Land Use Land Cover Maps 

CA-MC is a combined Cellular Automata and Markov Chain land cover prediction procedure that adds an 

element of spatial contiguity and knowledge of the likely spatial distribution of transitions to Markov chain 

analysis. The transition areas matrix calculated using the Markov model was integrated into the CA -based 

Markov model to predict land use land cover maps for 2010 and 2020. 

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3.2.1 Predicted land use land cover map of the year 2010 

To predict the LULC map for year 2000, the LULC map of 1990 is considered the base map. The transition 

probability calculated in Table 2 was considered as changing probability from one land cover category to another 

for the year 1990-2000. It was assumed that the same probability will take place to change particular land use. 

The actual and Predicted LULC map of the year 2000 are shown in Figure 4. 

 

Figure 4. Predicted land use land cover Maps of year 2010 

3.2.2 Predicted land use land cover map of the year 2020 

To predict the LULC map for the year 2020, the LULC map of 2010 is considered a base map. The 

transition probability calculated in Table 3 was considered as changing probability from one land cover category 

to another for the year 2000-2010. It was assumed that the same probability will take place to change particular 

land use. The actual and predicted LULC map for the year 2020 is shown in Figure 5. 

 

Figure 5. Predicted land use land cover Maps of the year 2020 

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3.3 Model Predication Accuracy of Predicted LULC 

Figure 6. shows the 95.30% probable accuracy that predicted LULC will be concentrated in the actual 

LULC map of the year 2010. Similarly, for the year 2020 (Figure 7.), an area under the curve is 86.90%. It shows 

that there will be 86.90% of areas that are available in the actual LULC map of the year 2020. 

 

Figure 6. Model Prediction Accuracy of predicted 

LULC 2010 

 

Figure 7. Model Prediction Accuracy of predicted 
LULC 2020 

3.4 Predicted Map of LULC 2035 and LULC 2050 

To predict LULC for 2035, transition areas (transition probabilities) were calculated using Markov chain 

analysis from 2010 to 2035, as shown in Table 5 and the predicted LULC map in Figure 8. It was assumed that 

the transition probability of LULC between 2010 and 2020 will remain the same for the next 15 years, also 

considering 2020 as a base year. There is a probability that 36.33% of agriculture areas will be expected to change 

into built-up areas, 33.50% of areas into vacant land and 3.66% areas into water bodies. In comparison, 26.51% 

of areas are expected to persist in 2020. In vacant land, 40.27% of areas will be transformed to build up, 3.97% 

of areas into the water, while 37.54% expect to remain as it is in 2020. Regarding water bodies, 10.64% will be 

converted into vacant land and 11.67% into agriculture in 2020. 

 

Figure 8. Predicted LULC map of 2035 

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Table 5. Transition Probability of Changing Land Use Land Cover Class from 2010-2035 

Land use/ Land cover Built-up Agriculture Vacant Land Water 
Built-up 95.86% 0.64% 3.34% 0.17% 

Agriculture 36.33% 26.51% 33.50% 3.66% 
Vacant Land 40.27% 18.23% 37.54% 3.97% 

Water 12.94% 11.67% 10.60% 64.79% 

To predict LULC for 2050, transition areas (transition probabilities) were calculated using Markov chain 

analysis from 2010 to 2050, as shown in Table 6 and predicted LULC map 2050, as shown in Figure 9. It was 

assumed that the transition probability of LULC between 2010 and 2020 will remain the same for the next 30 

years, also considering 2020 as a base year. There is a probability that 57.77% of agriculture areas will be 

expected to change into built-up areas, 23.30% of areas into vacant land and 4.62% of areas into water bodies. In 

comparison, 14.31% of areas are expected to persist in 2020. In vacant land, 60.24% of areas will be transformed 

to build up, 4.69% of areas into water, while 22.55% expect to remain as it is in 2020. Regarding water bodies, 

14.80% will be converted into vacant land and 12.44% into agriculture in 2020. 

 

Figure 9. Predicted LULC map of 2050 

Table 6. Transition Probability of Changing Land Use Land Cover Class from 2010-2050 

Land use/ Land cover Built-up Agriculture Vacant Land Water 
Built-up 93.54% 1.39% 4.64% 0.43% 

Agriculture 57.77% 14.31% 23.30% 4.62% 
Vacant Land 60.24% 12.52% 22.55% 4.69% 

Water 29.45% 12.44% 14.80% 43.31% 

3.5 Discussion 

Over thirty years, the city has experienced vast urbanization as the built-up area increased by 480.64% at 

the expense of vegetation loss by almost 97.16% from 1990 to 2020. Which proximity factors like national and 

state highways (Li et al., 2018), central business district (Thapa & Murayama, 2020), the airport (Zhou et al., 

2020), the railway station (Yang et al., 2020), the bus stations (Bharath et al., 2018), BRTS and Sitilink, 

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development of outer ring road and metro; government interventions like town planning schemes, development 

plan (Sheladiya, 2023), educational and health facilities, parks (Lu et al., 2018), low land price in outer skirt, 

diamond and textile industries (Sheladiya, 2023) as socioeconomic factors played significant role in urbanization 

of Surat city. The resulting value by the AOC curve indicated that the prediction accuracy of forecasted LULC 

maps of 2010 and 2020 was 95.30% and 86.90%, which was relatively higher and good compared to the CA-MC 

model used by Kallvetty and Bandopadhyay (2018) and Rahnama (2021). It was found that the incorporation of 

GIS and land use/ cover maps derived from remote sensing data with the CA– Markov model was capable of 

modeling and simulating spatial and temporal land use change efficiently for the years 2035 and 2050 (Kamusoko 

et al., 2009; Mitsova et al., 2011; Myint & Wang, 2006) Therefore, the predicted LULC maps for the year 2035 

and 2050 showed that city should have 48.16% and 61.28% concrete jungles because of rapid urbanization and 

mega infrastructure projects like metro, dedicated industrial freight corridor, diamond burge (Sheladiya, 2023). 

Taubenböck et al. (2012) monitor the rate of urbanization for twenty-seven mega cities across the world 

but not analyzed and modeled that how dynamic structure of LULC changes over the period. However, our study 

on Surat city focusing towards changes in dynamic structure of LULC with transition probability over the period 

of thirty years. Yin et al. (2011) carried out LULC analysis from 1979 to 2009 but not used mathematical 

modelling like CA-MC. It becomes vital when we are dealing with large scale temporal datasets to determine 

the accuracy of resulted maps.  

The reliability of land use change modeling methods can be improved by combining two or more 

simulation techniques to integrate the advantages of each model (Xin et al., 2012). It is worth mentioning that 

CA–Markov model has been used recently in dynamic spatial phenomenon simulation and future land use change 

prediction (Wang et al., 2012). Moreover, the integrated CA–Markov chain model takes advantage of the 

Markov chain of land use change quantities prediction and dynamic explicit spatial simulation of the CA model. 

Thus, the CA–Markov model can be appropriate for spatial modeling of land use change (Xin et al., 2012). 

Consequently, the incorporation of GIS and land use/ cover maps derived from remote sensing data with CA– 

Markov model is capable of modeling and simulating spatial and temporal land use change efficiently (Kamusoko 

et al., 2009; Mitsova et al., 2011; Myint & Wang, 2006). Moreover, the CA–Markov model provides reliable land 

use change simulation results and overcomes the lack of socioeconomic, statistical, and historical data (Sang et 

al., 2011). In the CA–Markov modeling process, the temporal changes of land use classes are directed in the 

Markov chain process based on produced transition matrices, whereas the spatial changes are controlled by 

transition potential maps, configuration of neighborhoods, and local transition rule during CA model process 

(Guan et al., 2020; White & Engelen, 2000). 

Town planners generally used zoning to differentiate land use as a method of guiding and controlling the 

growth of urban areas. In the early development phase, this concept was applied in the planning of developed 

countries and is now in developing countries (He et al., 2018). Therefore, this study will provide deep insights 

to urban and regional planners in developing strategies for evaluating various urban development scenarios 

about potential implications for land use and the advancement of existing spatial plans and policies in the Indian 

context (Al-Ahmadi et al., 2009).  

 

4. Conclusion 

Augmentation of classified land use land cover maps of 1990, 2000, 2010 and 2020 into CA–Markov chain 

model for Surat City area were modelled and simulated successfully. The overall modeling success was 95.30 % 

for the projected land use map 2035 and 86.90 % for the predicted land use map 2050. One good advantage of 

the applied CA–Markov chain model is that the model needs limited data to simulate and predict any future land 

use change explicitly, i.e., at least two land use maps in different time instants. On the other hand, the CA–

Markov chain model cannot analyze and explain urban land use change driving factors, such as biophysical and 

socioeconomic factors, which are very important to manage, guide current situations and prepare wise plans for 

future demands. The land use change analysis of the period 1990-2020 demonstrated a continuous decrease in 

vegetation and vacant lands. 

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Additionally, the annual decline rate of vegetation areas has increased in the last decade. The predicted 

land use situations in 2035 and 2050 reveal alarming accelerated loss of vegetation lands in the study area. 

Furthermore, based on predicted results, the future urban area would expand in a much-dispersed mode. 

 These findings indicate that the situation will worsen in the future. For that, controlling increased urban 

growth and protecting agricultural areas is necessary to promote rational land use and a sustainable urban 

environment. However, to better understand land use changes and their driving forces, it's necessary to 

incorporate biophysical and socioeconomic data in the CA–Markov chain model. This aim can be achieved by 

integrating another model involving these factors, like the logistic regression model, AHP model and other data 

mining approaches. 

5. Acknowledgments 

Thanks to all staff in the Urban Planning Section and Cartographic Lab of the Department of Civil 

Engineering for allowing us to conduct research. 

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